ICDAR2015 Competition on Keyword Spotting for Handwritten Documents

Description

The ICDAR2015 Competition on Keyword Spotting for Handwritten
Documents is organised in the framework of the
ICDAR 2015 competitions.
The goal of this competition is to fairly compare and promote
different approaches used in the field of Keyword Spotting (KWS).

KWS has been considered for both Speech and Text documents under
very different points of view and applications targets. Perhaps
the most important distinction is Query-by-Example/Query-by-String (QbE/QbS);
i.e., whether the query is by giving a word image example, or just a
character string. But many other distinctions are (very) relevant;
among other: Traning-based/training-free; i.e., whether the KWS system
is allowed or not to train on appropiate (annotated) images, and
Segmentation-based/segmentation-free; i.e., whether the KWS is
applied to full document (page) images or just to images of
individual words (previously cropped from the original full
images).

Clearly each of these flavours of the KWS problem statement has
its own difficulty degree and application targets. For instance,
QbS is mandatory for applications involving large-scale
handwritten image indexing and search under the precision-recall
tradeoff model. In this case, given the scale, it can be very
advantageus to use training-based KWS. Other kind of
applications involve assisting human transcribers by allowing
them to find words in a documment which have a shape similar to a
word or part of a word (perhaps one which the transcriber is not
sure how to transcribe when it appears for the first time). In
such applications, a training-free QbE system is most approriate.

In the present contest we aim at testing KWS systems, maybe
developped under different points of view, under uniform data
sets and benchmark assessment conditions. This is expected to
shear light on the relative capabilities of different approaches
and their appropriateness for the different kinds of applications.

The contest is divided into two tracks, depending on the requirements of
the keyword spotting approaches. Each track consists of two optional
assignments. Participants are able to submit solutions to one or both
assignments, depending on the restrictions of their systems.

Track I: Training-free track

This track is aimed for KWS approaches that do not require any training
data. The query keywords will be given as image examples (i.e. under
the QbE paradigm) to be spotted in the document images. Participants
in this track are able to complete two different assignments:

Segmentation-based: The words in the test document images will be segmented
and given as image patches to the participants. Participants will have to
provide a list of matches between the test patches and query images, sorted
by confidence.

Segmentation-free: No segmentation will be given on the test document images.
Participants will have to match the query images directly on the document images.
They will provide a list of bounding boxes indicating the matches, sorted by
confidence.

Track II: Training-based track

This track is aimed for KWS approaches that rely on training data to
build their KWS models. Training data consists of pairs of text (line)
images accompained with their corresponding transcripts. Participants
will have to match a list of query keywords (presented as strings or
image examples) across the set of document images. In this track,
participants may submit solutions to any of these two assignments:

Query-by-String: The keywords will be given as text strings. Participants will
have to provide a list of bounding boxes, sorted by confidence, indicating the
image regions where the query keywords are spotted.

Query-by-Example: The keywords will be given as exemplar images.
Participants will have to provide a list of bounding boxes, sorted by confidence,
indicating the image regions where the query keywords are spotted.